Copied files from source repository

README.md

@@ -1,13 +1,14 @@
 ---
-title: Xbt
-emoji: 📈
-colorFrom: indigo
-colorTo: red
+title: Serverless TextGen Hub
+emoji: ♨️🏆♨️
+colorFrom: pink
+colorTo: purple
 sdk: gradio
-sdk_version: 5.12.0
+sdk_version: 4.44.1
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
+short_description: Run Llama,Qwen,Gemma,Mistral, any warm/cold LLM. No GPU req.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,219 @@
+import gradio as gr
+from openai import OpenAI
+import os
+
+ACCESS_TOKEN = os.getenv("HF_TOKEN")
+print("Access token loaded.")
+
+client = OpenAI(
+    base_url="https://api-inference.huggingface.co/v1/",
+    api_key=ACCESS_TOKEN,
+)
+print("OpenAI client initialized.")
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    frequency_penalty,
+    seed,
+    custom_model
+):
+
+    print(f"Received message: {message}")
+    print(f"History: {history}")
+    print(f"System message: {system_message}")
+    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
+    print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
+    print(f"Selected model (custom_model): {custom_model}")
+
+    # Convert seed to None if -1 (meaning random)
+    if seed == -1:
+        seed = None
+
+    messages = [{"role": "system", "content": system_message}]
+    print("Initial messages array constructed.")
+
+    # Add conversation history to the context
+    for val in history:
+        user_part = val[0]
+        assistant_part = val[1]
+        if user_part:
+            messages.append({"role": "user", "content": user_part})
+            print(f"Added user message to context: {user_part}")
+        if assistant_part:
+            messages.append({"role": "assistant", "content": assistant_part})
+            print(f"Added assistant message to context: {assistant_part}")
+
+    # Append the latest user message
+    messages.append({"role": "user", "content": message})
+    print("Latest user message appended.")
+
+    # If user provided a model, use that; otherwise, fall back to a default model
+    model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
+    print(f"Model selected for inference: {model_to_use}")
+
+    # Start with an empty string to build the response as tokens stream in
+    response = ""
+    print("Sending request to OpenAI API.")
+
+    for message_chunk in client.chat.completions.create(
+        model=model_to_use,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+        frequency_penalty=frequency_penalty,
+        seed=seed,
+        messages=messages,
+    ):
+        token_text = message_chunk.choices[0].delta.content
+        print(f"Received token: {token_text}")
+        response += token_text
+        yield response
+
+    print("Completed response generation.")
+
+# GRADIO UI
+
+chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", likeable=True, layout="panel")
+print("Chatbot interface created.")
+
+system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
+
+max_tokens_slider = gr.Slider(
+    minimum=1,
+    maximum=4096,
+    value=512,
+    step=1,
+    label="Max new tokens"
+)
+temperature_slider = gr.Slider(
+    minimum=0.1,
+    maximum=4.0,
+    value=0.7,
+    step=0.1,
+    label="Temperature"
+)
+top_p_slider = gr.Slider(
+    minimum=0.1,
+    maximum=1.0,
+    value=0.95,
+    step=0.05,
+    label="Top-P"
+)
+frequency_penalty_slider = gr.Slider(
+    minimum=-2.0,
+    maximum=2.0,
+    value=0.0,
+    step=0.1,
+    label="Frequency Penalty"
+)
+seed_slider = gr.Slider(
+    minimum=-1,
+    maximum=65535,
+    value=-1,
+    step=1,
+    label="Seed (-1 for random)"
+)
+
+# The custom_model_box is what the respond function sees as "custom_model"
+custom_model_box = gr.Textbox(
+    value="",
+    label="Custom Model",
+    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
+    placeholder="meta-llama/Llama-3.3-70B-Instruct"
+)
+
+def set_custom_model_from_radio(selected):
+    """
+    This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
+    We will update the Custom Model text box with that selection automatically.
+    """
+    print(f"Featured model selected: {selected}")
+    return selected
+
+demo = gr.ChatInterface(
+    fn=respond,
+    additional_inputs=[
+        system_message_box,
+        max_tokens_slider,
+        temperature_slider,
+        top_p_slider,
+        frequency_penalty_slider,
+        seed_slider,
+        custom_model_box,
+    ],
+    fill_height=True,
+    chatbot=chatbot,
+    theme="Nymbo/Nymbo_Theme",
+)
+print("ChatInterface object created.")
+
+with demo:
+    with gr.Accordion("Model Selection", open=False):
+        model_search_box = gr.Textbox(
+            label="Filter Models",
+            placeholder="Search for a featured model...",
+            lines=1
+        )
+        print("Model search box created.")
+
+        models_list = [
+            "meta-llama/Llama-3.3-70B-Instruct",
+            "meta-llama/Llama-3.2-3B-Instruct",
+            "meta-llama/Llama-3.2-1B-Instruct",
+            "meta-llama/Llama-3.1-8B-Instruct",
+            "NousResearch/Hermes-3-Llama-3.1-8B",
+            "google/gemma-2-27b-it",
+            "google/gemma-2-9b-it",
+            "google/gemma-2-2b-it",
+            "mistralai/Mistral-Nemo-Instruct-2407",
+            "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "mistralai/Mistral-7B-Instruct-v0.3",
+            "Qwen/Qwen2.5-72B-Instruct",
+            "Qwen/QwQ-32B-Preview",
+            "PowerInfer/SmallThinker-3B-Preview",
+            "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+            "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            "microsoft/Phi-3.5-mini-instruct",
+        ]
+        print("Models list initialized.")
+
+        featured_model_radio = gr.Radio(
+            label="Select a model below",
+            choices=models_list,
+            value="meta-llama/Llama-3.3-70B-Instruct",
+            interactive=True
+        )
+        print("Featured models radio button created.")
+
+        def filter_models(search_term):
+            print(f"Filtering models with search term: {search_term}")
+            filtered = [m for m in models_list if search_term.lower() in m.lower()]
+            print(f"Filtered models: {filtered}")
+            return gr.update(choices=filtered)
+
+        model_search_box.change(
+            fn=filter_models,
+            inputs=model_search_box,
+            outputs=featured_model_radio
+        )
+        print("Model search box change event linked.")
+
+        featured_model_radio.change(
+            fn=set_custom_model_from_radio,
+            inputs=featured_model_radio,
+            outputs=custom_model_box
+        )
+        print("Featured model radio button change event linked.")
+
+print("Gradio interface initialized.")
+
+if __name__ == "__main__":
+    print("Launching the demo application.")
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+gradio
+openai